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Commentary

On The Emotional Intelligence Of Machines

In the 2016 film “Arrival,” based on Ted Chiang’s excellent novella “Story of Your Life,” Amy Adams’ character gives a summary definition of what’s known
in linguistics as the Sapir-Whorf Hypothesis, the theory that “the language you speak determines how you think.”

This concept appears elsewhere in fiction, such as Orwell’s
“1984,” in which the authoritarian state creates the language Newspeak to make it impossible for people to think critically about the government, or even to contemplate their own
subjugation.

And, more disturbingly, it plays a role in nonfictional contexts as well. During the Holocaust, Nazis described Jews as Untermenschen – subhuman –
and rats. Hutus involved in the Rwanda genocide called Tutsis “cockroaches,” and slaveowners throughout history referred to slaves as animals -- the same word our sitting president has
used to describe immigrants.

The opposite holds true, too. If
the language we use with other people conveys emotional awareness, empathy, authenticity, and self-regulation, it is humanizing and considered emotionally intelligent.

In the
marketing world, however, where effective communication is everything, we often forget this.

Consider the negative influence that programmatization, automation, and data
worship have had on the very way marketers speak, from the jargon of ad tech to the basic labels we apply to those people we want to sell our wares to: “targets” and
“consumers.” In particular, as my former
colleague Jonah Bloom wrote, the pervasive use of the word “consumer” both depersonalizes and reduces the other humans with whom we share the planet to “bipedal purses
whose only value is as buyers of stuff.”

It is from this lack of contextual awareness, of EQ (emotional intelligence quotient), that so much failed marketing
stems.

Given the expectation, particularly among millennials, of authenticity and personalization in their interactions with brands, as well as the growing use of machine
learning in all areas of digital marketing -- from automated media buying (done by, for example, Albert Technologies) to computer vision for branded object recall (a function of Clarifai,
for instance) to recurrent neural networks for language modeling -- now is the time for us to be thinking structurally about the other new things machine learning could enable, and to focus on
building those applications.

To this point, one burgeoning area of innovation is in “affective computing.” In order for an AI to interact with a person in a way that
feels truly authentic to the human, the AI must be able to convey empathy and detect emotion. The ability of a computer to do so falls into the field of affective computing.

Detecting emotion in human faces in one version of this, and not a brand new one. In 2016, Apple acquired Emotient, a startup that uses machine learning (ML) to analyze facial expressions and
detect emotions, as does Affectiva, the MIT Media Lab spin-off founded in 2009. And there’s the oft-cited example of ML applied to diagnostic imaging, enabling computers to analyze CAT scans
with much higher speed and accuracy than humans.

Often when we talk about ML, we’re really talking about automation or optimization, as shown above or with the example of
Instacart, which built a system on top of Google’s open-source tools Keras and Tensorflow to optimize the routing of its personal shoppers through grocery stores. The new system delivered a 50%
performance improvement.

Another area ripe for automation is in scaled, real-time communications between brands and their customers. Think of 5,000 people texting with Hertz
at the same time. No human team could handle that. The application of ML to solve these challenges goes beyond the analysis of images and words into generative adversarial networks, a branch of AI
that has been used to discover new drugs and compose music.

Imagine an AI that, while conversing with a human, dynamically generates original content of a high EQ that
feels, to that human, both authentic and personal. And then proceeds to do the same with 5,000 other people simultaneously.

Josh. We are doing some of this, and aspiring to do more. We aim for a day when we can produce charming robots, with the empathy of a Jewish mother or the wit of a scintillating socialite - or both - depending on the circumstances :-) This is being achieved in consultation with ethicists, combinining Positive* Psychology and Character Strenths* with machine learning - via an epic collaboration with Columbia University.